Michigan Technological University Digital Commons @ Michigan Tech Dissertations, Master's Theses and Master's Reports Dissertations, Master's Theses and Master's Reports - Open 2014 EVALUATION OF POTENTIAL TRANSLOAD FACILITY LOCATIONS IN THE UPPER PENINSULA (UP) OF MICHIGAN Irfan Rasul Michigan Technological University Copyright 2014 Irfan Rasul Recommended Citation Rasul, Irfan, "EVALUATION OF POTENTIAL TRANSLOAD FACILITY LOCATIONS IN THE UPPER PENINSULA (UP) OF MICHIGAN", Master's report, Michigan Technological University, 2014. http://digitalcommons.mtu.edu/etds/805 Follow this and additional works at: http://digitalcommons.mtu.edu/etds Part of the Civil Engineering Commons EVALUATION OF POTENTIAL TRANSLOAD FACILITY LOCATIONS IN THE UPPER PENINSULA (UP) OF MICHIGAN By Irfan Rasul A REPORT Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE In Civil Engineering MICHIGAN TECHNOLOGICAL UNIVERSITY 2014 © 2014 Irfan Rasul This report has been approved in partial fulfillment of the requirements for the Degree of MASTER OF SCIENCE in Civil Engineering Department of Civil and Environmental Engineering Report Advisor: Dr. Pasi T. Lautala Committee Member: Dr. Gregory A. Graman Committee Member: Dr. William Sproule Department Chair: Dr. David Hand Contents Acknowledgements .......................................................................................................... viii Abstract .............................................................................................................................. ix CHAPTER ONE: INTRODUCTION ................................................................................. 1 1.1 Background of the Research ................................................................................ 1 1.2. Statement of the Problem ..................................................................................... 2 1.3. Objectives of the Study ........................................................................................ 4 CHAPTER TWO: LITERATURE REVIEW ..................................................................... 5 2.1 Transportation Logistics in the Supply Chain ...................................................... 5 2.2 Principles of Intermodal / Multimodal Transportation ........................................ 7 2.3 Facilities and Equipment for Intermodal/ Multimodal Transport ...................... 10 2.4 Transport Model ................................................................................................. 12 2.5 Shipping Costs for Truck and Rail ..................................................................... 13 2.6 Carbon Emission Factors ................................................................................... 16 2.7 Carbon Emission Cost ........................................................................................ 19 CHAPTER THREE: METHODOLOGY ......................................................................... 20 3.1. Single mode and multimodal transportation chains ........................................... 20 3.2. Transport Model for the Study ........................................................................... 22 3.3. Notation of Parameters for Equation Formulation ............................................. 25 3.4. Equation Formulation ......................................................................................... 27 CHAPTER FOUR: TRANSLOAD FACILITY ANALYSIS FOR MICHIGAN’S UPPER PENINSULA .................................................................................................................... 30 4.1. Introduction ............................................................................................................ 30 4.2. Modelling Scenarios .............................................................................................. 35 iii 4.3. Input Data Sources and Preparation ....................................................................... 36 4.3.1. Infrastructure ...................................................................................................... 36 4.3.2. Shipments ........................................................................................................... 36 4.3.3. Costs Parameters ................................................................................................ 41 4.4. UP Case Study results ............................................................................................ 45 4.4.1. Modeling Outcomes for County Based Analysis ............................................... 45 4.4.2. Effect of Emission Costs .................................................................................... 51 4.4.3. Transload facility location comparison .............................................................. 51 4.4.3.1. 4.5. Sensitivity Analysis of Transload Facilities for Different HDF Prices .......... 53 Findings from the UP Case Study .......................................................................... 55 CHAPTER FIVE: CASE STUDIES FOR TWO MICHIGAN UP COMPANIES .......... 57 5.1. Introduction ............................................................................................................ 57 5.2. DA Glass America ................................................................................................. 58 5.3. Northern Hardwoods .............................................................................................. 58 5.3.1. Development of Parameters for Northern Hardwoods ....................................... 59 5.3.2. Study results for Northern Hardwoods............................................................... 59 CHAPTER SIX: CONCLUSIONS AND FUTURE RESEARCH................................... 64 6.1. Conclusions ............................................................................................................ 64 6.2. Future Research ..................................................................................................... 65 References ......................................................................................................................... 66 iv List of Figures Figure 1: Location of the Upper Peninsula (UP) of Michigan............................................ 3 Figure 2: Freight transportation service spectrum .............................................................. 6 Figure 3: Growth of intermodal transport in the U.S. from 1990 to 2012 (American Association of Railroads) .................................................................................................... 8 Figure 4: Intermodal transport chain................................................................................... 9 Figure 5: Rail-Truck multimodal transport using transload facility ................................. 10 Figure 6: Typical diagram of transload facility ................................................................ 11 Figure 7: Variations of Emission Factors for Rail and Truck from Past Studies ............. 18 Figure 8: Truck only and multimodal alternatives ............................................................ 20 Figure 9: Comparison between "truck only" and truck-rail" transport ............................. 21 Figure 10: Comparison between "truck only" and "truck-rail-truck" transport ................ 22 Figure 11: Conceptual spreadsheet transport model diagram ........................................... 23 Figure 12: Upper Peninsula rail and road network ........................................................... 31 Figure 13: Potential transload facility locations ............................................................... 33 Figure 14: Ishpeming rail yard owned by CN Railroad .................................................... 34 Figure 15: Amasa railsiding owned by E&LS Railroad ................................................... 35 Figure 16: Out of state origins and destinations (CN Website) ........................................ 41 Figure 17: Cost per ton-mile vs distances from CN Tariff rate ........................................ 43 Figure 18: Percent cost savings for Chicago movements (with direct rail access at final destinations) ...................................................................................................................... 46 Figure 19: Benefitted counties for Chicago movements................................................... 47 Figure 20: Percent cost savings for Minneapolis movements (with direct rail access at final destinations) .............................................................................................................. 49 Figure 21: Benefitted counties for Minneapolis movements ............................................ 50 Figure 22: Percent cost savings from each transload facility for Chicago and Minneapolis movements (excluding and including emission)............................................................... 52 Figure 23: Total percent cost savings from each transload facility for interstate movements (Chicago and Minneapolis) ........................................................................... 53 v Figure 24: Sensitivity analysis of percent cost savings from each transload facility for different HDF prices for Chicago movements .................................................................. 54 Figure 25: Sensitivity analysis of percent cost savings from each transload facility for different HDF prices for Minneapolis movements ........................................................... 55 Figure 26: Case study companies' locations ..................................................................... 57 Figure 27: Distances for Northern Hardwoods for truck only and multimodal option with rail access in final destination (Wisconsin movements) ................................................... 60 Figure 28: Multimodal cost savings for Northern Hardwoods (Wisconsin movements) using transload facility ...................................................................................................... 61 Figure 29: Distances for Northern Hardwoods for truck only and multimodal option (Minneapolis movements) ................................................................................................ 62 Figure 30: Multimodal cost savings for Northern Hardwoods (Minneapolis movements) using transload facility ...................................................................................................... 63 vi List of Tables Table 2: Unit Shipping Cost from Past Studies [51-57] ................................................... 15 Table 3: Unit Emission Factors from Past Studies ........................................................... 17 Table 4: Input parameters for developing conceptual spreadsheet transport model ......... 24 Table 5: Outputs from the conceptual spreadsheet transport model ................................. 24 Table 6: Interstate movement by truck and rail in the UP, 2009 ...................................... 38 Table 7: TRANSEARCH volume for the UP counties for selected interstate movements ........................................................................................................................................... 39 vii Acknowledgements I would like to acknowledge the efforts given by my advisor, Dr. Pasi Lautala, without whom the project was impossible for me to complete. He always encouraged me to come up with new ideas and implement those in my project. He took every available opportunity to teach me the system in the U.S. and let me do whatever I needed to go further with my research. I am grateful to my committee, Dr. Gregory Graman and Dr. William Sproule, who believed that my research would be beneficial for the Upper Peninsula region in the future. I have been working with Dr. Graman for more than a year on the UP Freight Rail Study” project and he allowed me to work independently and provided me with assistance whenever required. Dave Nelson joined us last year as Senior Research Scientist and from the beginning he provided me with valuable comments on my research which gave me the direction I needed to undertake for my analysis. He spent a lot of time with me to provide suggestions and improvements that were required for the project. My better half Mehjabeen Rahman remained with me every step of the way and inspired me in completing the report. There are many people whom I would like to express my gratitude for assisting me in completing my project. Sean Pengelly, provided assistance in collecting information which I was struggling with. Mr. John Kantola from Northern Hardwoods and Mr. Steve Williams from DA Glass America provided with information for developing case studies. Last but not the least I would like to thank Michigan Department of Transportation (MDOT) and National University Rail Center (NURail) for sponsoring the project, without it my research would have been impossible to complete. viii Abstract Shippers want to improve their transportation efficiency and rail transportation has the potential to provide an economical alternative to trucking, but it also has potential drawbacks. The pressure to optimize transportation supply chain logistics has resulted in growing interest in multimodal alternatives, such as a combination of truck and rail transportation, but the comparison of multimodal and modal alternatives can be complicated. . Shippers in Michigan’s Upper Peninsula (UP) face similar challenges. Adding to the challenge is the distance from major markets and the absence of available facilities for transloading activities. This study reviewed three potential locations for a transload facility (Nestoria, Ishpeming, and Amasa) where truck shipments could be transferred to rail and vice versa. These locations were evaluated on the basis of transportation costs for shippers when compared to the use of single mode transportation by truck to Wisconsin, Chicago, Minneapolis, and Sault Ste. Marie. In addition to shipping costs, the study also evaluated the potential impact of future carbon emission penalties on the shipping cost and the effects of changing fuel prices on shipping cost. The study used data obtained from TRANSEARCH database (2009) and found that although there were slight differences between percent savings for the three locations, any of the them could provide potential benefits for movements to Chicago and Minneapolis, as long as final destination could be accessed by rail for delivery. Short haul movements of less than 200 miles (Wisconsin and Sault Ste. Marie) were not cost effective for multimodal transport. The study also found that for every dollar increase in fuel price, cost savings from multimodal option increased by three to five percent, but the inclusion of emission costs would only add one to two percent additional savings. Under a specific case study that addressed shipments by Northern Hardwoods, the most distant locations in Wisconsin would also provide cost savings, partially due to the possibility of using Michigan trucks with higher carrying capacity for the initial movement from the facility to transload location. In addition, Minneapolis movements were found to provide savings for Northern Hardwoods, even without final rail access. ix CHAPTER ONE: INTRODUCTION 1.1 Background of the Research Transportation has been identified as the “critical link” in successful supply chains of the 21st century [1] and as part of the effort to reduce the cost of individual supply chain components, increasing transportation costs have become a growing concern for both shippers and transport service providers. There are various reasons for the increase, but one of the leading causes has been the cost of crude oil which has increased from $26 per barrel in 2002 to $104 (approx.) in 2014 (Energy Information Administration [2]. Today, trucks move around 60 percent of intercity freight tonnage and even higher percentage of the freight value, but trucks are also facing growing challenges [3]. While road/highway networks provide unparalleled access to shippers, movements by motor carriers tend to be more expensive per unit and are believed to cause higher carbon emissions and safety risks. In addition, congestion, stricter engine requirements, and high driver turnover are causing significant concerns for trucking, especially for long interstate movements. These increasing costs have forced logistics managers and transportation planners to look into more affordable, but still reliable modes of transportation. Especially rural area businesses are in challenging environment, as they are often far from key markets and their modal options and related infrastructure access are even more limited. While rail transportation offers an alternative for these regions, it has its own challenges. Rail cannot reach all parts of a country due to rail network limitations and railroad companies are reluctant to invest in rural areas, unless there are major shippers with significant volumes. In addition, many of the rural light-density lines have been abandoned as nonprofitable, further eroding the opportunities. One of the potential solutions is using a combination of two or more modes of transportation, also known as intermodal or multimodal transport. Intermodal and multimodal transport have increased over the past several decades mainly due to growth 1 of global trade and need for more robust transportation alternatives that combine efficiency with speed. More recently, the attention has shifted toward improvements for domestic transportation. This report discusses investigates whether multimodal transportation can provide benefits to the shippers in the Upper Peninsula of Michigan. 1.2. Statement of the Problem The Upper Peninsula (UP) of Michigan is located in the northern part of Michigan and can be considered a rural region. The only land border with the 48 states is with Wisconsin in the southwestern portion of the Western Upper Peninsula. Additional land connections to the peninsula are to Ontario, Canada through Sault Ste. Marie and to Lower Peninsula of Michigan through Mackinac Bridge. The remainder of the region is surrounded by the Great Lakes, Lake Superior in the north, Lake Michigan in the southeast and Lake Huron in the East. The location of the UP is shown in Figure 1. 2 Figure 1: Location of the Upper Peninsula (UP) of Michigan There are 15 counties in the UP. The peninsula covers 16,452 square miles (17 percent of Michigan total), but only three percent of its population (approximately 308,000). Some of the larger cities include Marquette and Escanaba. Main commodities include minerals (copper and iron), lumber and pulp products, clay, cement and stone products. These commodities account for a great majority of the total interstate movements. Despite its remote location and high proportion of low value and long distance shipments, the majority of the UP freight tonnage moves by trucks. While rail alternatives exist, their use is limited mainly by the service levels and accessibility. There are no current alternatives for intermodal transfers and even transload options are limited. There is a growing interest to such alternatives, but it hasn’t been evaluated whether sufficient volumes exist to make them sustainable. This study, together with the Michigan Department of Transportation (MDOT) and National University Rail Center (NURail) 3 funded project “Rural Freight Rail and Multimodal Transportation Improvements” are first steps toward such evaluations. 1.3. Objectives of the Study This objective of the study was to investigate the potential to establish a transload facility in the UP. More specifically, the study o Compared three locations in the UP for a potential facility: Nestoria, Ishpeming and Amasa. o Used freight movement data and case studies to quantify potential cost savings from multimodal transportation options. o Investigated the effect of fuel prices on percent cost savings. o Calculated the effect of emission costs to savings. 4 CHAPTER TWO: LITERATURE REVIEW 2.1 Transportation Logistics in the Supply Chain Transportation is one of the key components of efficient supply chains and efficiency must be achieved through proper management of the supply chain. Mentzer et al. (2001) defined supply chain as “a set of three or more entities (organizations or individuals) directly involved in the upstream and downstream flows of products, services, finances, and/or information from a source to a customer”. [4] Supply chain management, on the other hand, is defined by Christopher (2012) as “the management of upstream and downstream relationships with suppliers and customers in order to deliver superior customer value at less cost to the supply chain as a whole”. [5] Logistics is defined by Council of Logistics Management as “a part of the supply chain process that plans, implements, and controls the efficient, effective forward and reverse flow and storage of goods, services, and related information between the point of origin and the point of consumption in order to meet customers’ requirements”. [6] Tilanus defined Logistics as “the process of anticipating customer needs and wants; acquiring the capital, materials, people, technologies, and information necessary to meet those needs and wants; optimizing the goods- or service-producing network to fulfill customer requests; and utilizing the network to fulfill customer requests in a timely way”. [7] The main components of logistics are “logistics services, information systems and infrastructure / resources” [8] According to Tseng et al., the importance of logistics is increasing due to the interplay of transportation systems and logistics management. Since transportation costs account for a significant portion of the overall logistics cost, it is essential to improve transportation efficiency. Transportation can be conducted by a single mode or by a combination of multiple modal alternatives. The five main types of freight transportation modes include truck, rail, marine, air, and pipeline. Although the decision between the most suitable modes are highly dependent of the environment, each mode tends to serve a certain spectrum of the 5 freight transportation needs, as described in the Figure 2, developed by Cambridge Systematics. [9] Figure 2: Freight transportation service spectrum For surface transportation, the main modal alternatives are truck and rail, and potential combination of the two. For the past several decades, trucks have greatly dominated the overall freight transportation market in the U.S. According to the Federal Highway Administration (FHWA) Freight Analysis Framework, trucks transported approximately 72% of all freight tonnage and over 70% of freight commodity value. While trucks are dominant, they are also facing challenges. One of the greatest challenges for trucks is their limited capacity to carry weight. Most interstate highways currently allow 80,000 pounds total truck weight. [10] To accommodate more freight on the road, policy makers are considering increase in the truck weight limits to 90,000 to 97,000 pounds, [11, 12] Another challenge is hours of service. According to Federal Motor Carrier Safety 6 Administration’s (FMCSA) rule of “hours of driving”, truck drivers cannot drive for more than 11 hours at a time. [13] Rail transportation can provide a cost-efficient alternative to trucking, but accessibility, shipment time, and service quality all limit its use for a great portion of surface freight. 2.2 Principles of Intermodal / Multimodal Transportation In the early 1950s, most of the commodities were being transported in ships as “breakbulk” either with or without being packaged in small boxes that are smaller than today’s containers. The term “break-bulk” means that the commodities might be in bulk (mainly dry ones) or packaged in a box. [14] A large space in the ship was required to accommodate both bulk and packaged commodities. Moreover, this increased the time and port handling cost for large volumes of bulk commodities. As described by Levinson, the port side handling could reach 60 to 75 percent of the total transportation cost. [15] Malcom McLean, of the commercial truck industry, introduced the concept of containerization in 1960s. The method was similar to the box concept used by the military, but in larger sizes, which could be transported by all of the three modes: truck, rail, and marine. [16] The impact of containerization was seen in the drastic reduction of port side cost and since then intermodal transport has become popular. The main difference between intermodal and traditional multimodal shipments is that in intermodal transportation the commodities are not taken out or handled while transferring the container from one mode to another. [17] One common definition of intermodal is “… the movement of goods in one and the same loading unit or vehicle that successively uses two or more modes of transport without handling the goods themselves in changing modes”. [18] Over the past two decades there has been a significant increase in intermodal shipments and at the end of the 20th century, more than 90 percent of intermodal transport was containerized international movements. [15]. Figure 3 presents the growth of intermodal transport in the U.S. from 1990 to 2012. [19] 7 Figure 3: Growth of intermodal transport in the U.S. from 1990 to 2012 (American Association of Railroads) Truck or rail is commonly used to transport commodities from shippers to the intermodal port terminals where the commodities are loaded on ship (usually by gantry cranes). The ship carries the long haul movement to another port where containers are unloaded and moved by rail to intermodal terminal for final delivery by trucks (called drayage). Alternatively, trucks may deliver the goods directly from the ports. A diagram showing a typical international intermodal transportation chain is illustrated in Figure 4. [20] 8 Figure 4: Intermodal transport chain Intermodal transportation can use both containers and trailers, but only containers can be used for maritime movements and for double stack railcars which efficiently handle two containers resulting in doubled capacity of freight on a single railcar with a minimal increase in shipping cost. [1, 21] As shown in Figure 4, this had led to the increase in the use of containers, while the use of trailers has decreased. Today, trailers are mainly used for domestic freight. While the intermodal transportation has found greatest success in international supply chains, domestic intermodal transport is also playing an important role and the number of shipments has doubled from 2004 to 2013. [22] It is also forecasted that the future growth in intermodal transportation will concentrate heavily on domestic movements. Multimodal transportation shares many of the principles with intermodal transportation, but instead of keeping goods in a single unit (container or trailer), they move from one vessel to another at transload locations. Figure 5 presents one example of the components of the transportation chain involving a transload facility for multimodal transport. [23-25] Shippers transport their commodities as bulk or break-bulk to the transload facility where it is moved to another bulk transportation unit (such as hopper or box car). In some cases, it can be also consolidated into a container for intermodal movement. The final delivery 9 may take place by rail, if the final destination has rail access, or the unit is taken to an intermediate transloading location where it is unloaded to a truck and transported to the final destination. Figure 5: Rail-Truck multimodal transport using transload facility 2.3 Facilities and Equipment for Intermodal/ Multimodal Transport If truck is used in conjunction with rail, a location known as an intermodal (container) or transload (container/ bulk) terminal is required to provide the transfer between truck and rail. Intermodal terminals are typically large facilities to accommodate full trains, repair facilities and conainer/trailer storage. A study by Steele (2010) mentioned that a feasible intermodal facility can require 100,000 carloads annually which should be travelling for 2,000 miles. [26] Another study by Middendorf (1998) investigated intermodal facilities operated by BNSF, Norfolk Southern, CSX and Union Pacific concluded that intermodal facilities should be able to handle at least 100,000 carloads annually and should have a minimum of 25 car spots.[27] Middendorf’s findings were for big metropolitan areas; Chicago; Long Beach; and Los Angeles, which handle more shippers than the rural areas. According to Steele et al. a transload terminal can be defined as “a receiving and distributing facility for lumber, grain, concrete, petroleum, aggregates, and other such bulk products”. [26] Transload facilities usually serve bulk and break-bulk commodities 10 and they can be of varying size, ranging from as simple as a siding track with truck access road to large facilities with warehouses and multiple tracks for different commodities. Typically, transload facilities which are able to handle both containers and bulk commodities have some common features for accommodating both truck and rail. For truck, they have amenities such as a certified weighing scale for trucks; a small office; parking lot; truck cleaning facility; driver rest area, and storage space. For rail, they have connection to the main line, storage/loading/unloading tracks, transloading equipment and so on. An example layout of a transloading facility is presented in Figure 6. [28] Figure 6: Typical diagram of transload facility Thomson (2012) concluded that for bulk transload facilities, 5 to 10 acres of storage space is required and at least 1,500 carloads annually should be generated from the facility to make it feasible. [29] HDR Engineering (2007) found that a transload facility could be established for as few as 250 to 300 rail cars per year. [30] Today, the nearest terminal for UP shippers is located 200-450 miles away (depending on origin within UP) in Chippewa Falls, serviced by CN and operated by a private contractor (Figure 44). This facility in Chippewa Falls had a projected volume of 5,000 containers 11 per year [31] Since Chippewa Falls handles only outbound traffic for international markets, most intermodal freight to/from the UP travels first either to terminals in Chicago, or Minneapolis. The long initial/final drayage is considered a major competitive hindrance by the UP companies. 2.4 Transport Model As the objective of the investigation was to perform comparative cost analysis, transportation modelling methods were reviewed to identify techniques and applications for the analysis. There are several alternatives for transport modelling, such as Classical Economical model; Inventory-Theoretic model; Trade-Off model; and Constrained Optimization model. [32] Of these four models, Trade-off and Constrained Optimization models have been most commonly used by the past studies related to optimizing transport mode choices and reducing transport costs.[33-38] Beside these, center of gravity; minimizing load-distance method; multi-objective optimization by linear and integer programming; and decision tree model have been used to locate optimal location of transload or intermodal facility. [39-42] Literature review revealed a number of parameters that were incorporated in developing a transport model. Owens et al. (2013), Chen et al. (2008), Hicks (2009) and Arnold et al. (2004) developed transport models for a particular situation, rather than for a general scenario. [17, 43-45] Owens (2013) incorporated track data (distance, elevation, speed, and curvature), travel time, rail car capacity, delay, fuel cost, loading and unloading cost for containers in their model. The study by Chen et al. used number of origins, destinations and commodities, inventory, unit inventory cost and capacity to formulate their model. Hicks used origin and destination coordinates for truck trips, unit costs for rail and truck, transfer locations, and fuel surcharges. Arnold et al. (2004) included unit shipping cost, origin/destination matrix, handling cost and location of terminals. All of the models highlighted above had an objective of optimizing cost based on the selected parameters. Owens derived total resistance for trains and sensitivity of operating cost for different fuel prices. Chen et al. found the optimized shipping and inventory cost. Hicks (2009) optimized transport routes based on cost for log movements and derived the 12 optimal split between truck and multimodal alternatives. Arnold et al. developed five scenarios which were variations of relative cost of rail, variation of handling costs, variation of rail border effect, location of new terminals, and optimization of the existing terminals. These scenarios were compared with the existing truck movements. The Great Lakes Geographic Intermodal Freight Transport Model (GIFT) developed by Winebrake et al. (2008) optimized operating cost based on time and emissions (CO2, NOX, PM10 and SOx) by using tradeoff concepts for the Great Lakes region. The GIFT model used the “Network Analysis” tool built in ArcGIS 9.2 by putting weights on each route and optimized the total transport route by selecting the route with the least weight value based on shipping and emission costs. Global Insight (2006) conducted studies that investigated operating cost for modes, expressed as US dollar per TEU (Twenty Foot Equivalent). The result of the model gave an optimized route for the movements from Cleveland, OH to Toronto. [46] Li et al. (2005) developed a model for transporting 12 types of coal from 29 companies in China. They used a multi-commodity logistic chain for minimum-cost optimization. The objective was to minimize the cost, optimize the capacity of the line, and not allow different types of coal to mix. The outcome of the study provided optimized route and cost savings from using such a route over the existing one. The study by Li did not include a transload facility as commodities moved on a single line by rail. [47] 2.5 Shipping Costs for Truck and Rail The two most common rates types applied for truck shipments are “Truck Load (TL)” and “Less-than-Truck Load (LTL)”. For bulk commodities “per-mile” rate is usually charged which excludes fuel surcharges and handling cost (handling cost is assumed to be paid by the consignee). For rail, there are ty dicallyifferent rates applied for container and carload shipments. Both truck and rail may also offer contract rates which are made for individual shippers with high volumes, or for other reasons to give a preferential treatment. [1] Literature review was conducted to identify typical shipping unit costs of surface transportation modes (rail and truck). The review revealed seven studies that identified 13 shipping unit costs for either rail, truck, or both modes (Table 2). Typically, truck and rail transport costs were expressed as US dollars (USD) per ton-mile. [48-50] From the historical costs, it was understood that specific cost from past studies can not be used for this studies as there are lots of variation. Instead, this study used the current CN tariff rates and truck shipping cost from shipper interviews to generate the transport unit costs. 14 Table 1: Unit Shipping Cost from Past Studies [51-57] Rail Author Study Year Commodity Bulk Bulk Forkenbrock 1994 Container 15 Container Distance (miles) Weight used (tons per car) Unit cost per ton-mile (indexed to 2012) Distance (miles) Heavyunit Mixed freight Intermodal DoubleͲ stack container 1000 105 0.0119 (0.02) < 250 Truck Weight Used (tons per truck) 14.8 500 70 0.012 (0.02) _ _ _ 1750 28 0.0268 (0.04) 250 - 500 14.8 0.0894 (0.13) 1750 56 0.0106 (0.011) > 500 14.8 0.0769 (0.11) Type Unit cost per ton-mile (indexed to 2012) 0.0217 (0.03) Wang et al 1995 Bulkand recycledpaper _ _ 60 0.12 (0.17) _ 22 0.1 (0.14) US DOT 19952004 GeneralFreight _ _ _ 0.032 (0.04) _ _ 0.1104 (0.19) Mixed freight _ _ 0.022 (0.03) < 250 24 0.0217 (0.03) Intermodal _ _ 0.0268(0.04) > 500 17.5 0.0842 (0.12) _ _ _ 0.024 (0.03) _ _ 0.08 (0.10) 50 tons 10,000 tons 50 tons 10,000 tons _ 75 75 2000 2000 _ 100 100 100 100 _ 1.09 (1.15) 0.0272 (0.03) 0.48 (0.52) 0.0032 (0.01) 0.034 (0.04) 75 75 2000 2000 _ 25 25 25 25 24 0.033(0.04) 0.033(0.04) 0.04 (0.05) 0.04 (0.05) 0.0842 (0.09) Kehoe, Owen Cambridge Systematics Inc. 2001 2002 GeneralFreight (Forkenbrock Study) Intermodal Bulk and Intermodal Columbia River Crossing 2004 Bulk Atkinson et al 2006 Bulk 15 An additional cost for multimodal transport is the handling cost during rail-truck (or truck-rail) interchange, also known as trans-shipment cost. Beresford (2011) and Hanssen et al. (2012) addressed the importance of the handling cost while considering multimodal transport as they can increase the multimodal transport cost to a great extent. [49, 58] According to literature, common tariff rates for loading and unloading varied from $1.5 to $6 per ton. [43, 59]. 2.6 Carbon Emission Factors Carbon emissions have been widely recognized as a challenge for the society. Transportation is responsible for 30 % of energy consumption in the U.S. and more than 70% of carbon emissions. [60, 61] Transportation has overall one of the lowest efficiencies for energy use, so any potential reductions in emissions due to modal selections play an important part of the U.S. sustainability. [62] Total carbon emissions caused by transportation can be calculated based on emission factors and fuel consumption and it is believed that multimodal transport has the potential to reduce carbon footprint due to typically lower energy consumption by rail than by trucks. [63, 64] The literature review on past studies with emission factors for rail and truck revealed six studies, summarized in Table 2. 16 Table 2: Unit Emission Factors from Past Studies Author Forkenbrock [51] Study Year Emission Factor (g/ ton-mile) Chemical Constituents 1994 Rail Truck Heavy unit train N/A Mixed freight train 18.6 CO2 134.4 Intermodal train 17 Double-stack container train 15.40 Rourke et al. [65] 2002 CO2 General Freight 25.40 297.9 Hanaoka et al. [66] 2006 2009 CO2 General Freight 41.42 172 Texas Transportation Institute [67] 2005 CO2 General Freight 24.39 64.96 Cefic-ECTA [68] 2012 CO2 General Freight 32 90.18 Blanco, E.E. [69] 2012 CO, CO2 Intermodal 25.2 149.7 From Table 2 , the range of emission factors for rail range between 15.40 - 41.42 grams per ton-mile with a median value of 24.39 grams and for truck between 64.96 - 297.90 grams per ton-mile with a median value of 134.40 grams. Figure 7 shows the minimum, maximum and median values from the studies presented in . 17 Figure 7: Variations of Emission Factors for Rail and Truck from Past Studies One of the key factors explaining to wide range of emission values was study location. Data suggests that emission values are generally considered higher in metropolitan areas than the rural areas. Studies by Rourke et al, Texas Transportation Institute and CeficECTA evaluated 15 to 25 metropolitan areas and resulted in higher rail emission factors, ranging between 24.39 to 41.42 grams per ton-mile.[65-68] The studies conducted on more than 2,000 rural counties used lower range between 15.4 to 22.3 grams per tonmile. Forkenbrock concluded that truck payload and speed are also relevant in calculation of emission factors. Cefic-ECTA study considered empty backhaul while calculating the emission factors. Other factors included in the studies were percent of pay load used, and amount of backhaul (Cefic-ECTA). Texas Transportation Institute and Transportation Research Board concluded that improved fuel efficiency, higher payload, and technology in today’s trucks are allowing lower emissions. On the other hand, rail tends to have 18 lower fuel consumption and thus results in less emission. Overall, every study had different approach to address their emission factors, making it difficult to calculate general values. As an example of a practical application, Class I railroad CN uses 28.73 grams per ton-mile as emission factor for rail and 183.54 grams per ton-mile for trucks in their carbon calculator. [70] For rail it is close to the median of past studies but for truck it falls in the lower range. This study used the average of the median of emission factors from past studies and CN emission factors for both truck and rail. CN operates in the UP and thus make it more suitable to take it into account along with the past studies. 2.7 Carbon Emission Cost Currently, there is no monetary penalty imposed for carbon emissions, but several studies have addressed the cost of carbon. The studies provided the monetary values as US dollars (USD) per ton of CO2 emission and the values varied widely between $2.27 and $36. Chernick and Caverhill provided a range of $2.27 to $24.95 per ton of CO2 emission, based on relative physical and chemical toxicity, public concern, direct estimation of the effects, and societal value from the maximum cost derived from the effects. [71]. National Economic Research Associates suggested emission cost of $3.56 per ton of CO2 emission.[55] National Research Council provided a range of $10 to $20 per ton of CO2 emission, but did not include their basis for the monetary value. [72] Forkenbrock reviewed the three earlier studies and found the lowest value ($10) of the study by National Research Council per ton of CO2 ($15.25 when indexed to 2012) the most appropriate value for emission cost. [51] Finally, a recent CBC News article presented the definition of emission cost as “an estimate of the monetized damages such as property damage or changes to agricultural productivity and human health associated with increased carbon emissions”. [73] The article provided the estimation as $22 to $36 per ton of CO2. 19 CHAPTER THREE: METHODOLOGY 3.1. Single mode and multimodal transportation chains There are two common combinations of multimodal truck/rail transportation that provide alternatives to single mode “truck only” alternative; “truck- rail” and “truck- rail-truck”. “rail-truck” might also be option, but it occurs more rarely. Figure 6 presents a schematic of the most common alternatives. O and F represent origin and final destination, respectively, and points 1, 2, 3 and 4 are interchange points between truck and rail. For truck only transportation, the whole trip takes place in one movement, without intermediate stops or transloads. When rail has access to final destination there, freight is moved from truck to rail at transload facility (point 1), but there is no need to move shipment back to trucks for final delivery. For truck-rail-truck options, freight is transloaded to rail at point 1 and back to truck at point 2,3, or 4.The difference between the latter three options is the length of rail haulage and final truck drayage from intermediate transload facility to final destination. Figure 8: Truck only and multimodal alternatives In Figure 9, the comparison between “truck only” and “truck –rail” scenarios is illustrated from purely cost perspective (no serv. Rail tends to offer lower cost per unit of shipment, explaining the shallower angle in cost graph. If rail is used to transport commodities to the final destination from a transload facility, the transport cost can be less than the truck 20 only transport if the distance is long enough to cover the transloading cost at the transload facility. Figure 9: Comparison between "truck only" and truck-rail" transport Figure 10 illustrates comparison between “truck only” and “truck-rail-truck” transport. As seen in Figure 10, the two main determinants of total shipment cost are - distance traveled by rail and handling cost. If shipment is shifted to final truck drayage at point 2, multimodal cost is higher than truck transport. At point 3, multimodal cost is equal to the truck transport, known as break-even distance and at point 4, it is lower than truck alternative. As the rail distance increases and drayage decreases, the balance shifts toward multimodal transportation. 21 Figure 10: Comparison between "truck only" and "truck-rail-truck" transport 3.2. Transport Model for the Study A spreadsheet calculation model was developed to compare the costs of truck only and multimodal option. The objective was to calculate shipping and emission costs for truck and multimodal (using transload) alternatives. Figure 11 illustrates the concept diagram for calculations. The input parameters are the movements, infrastructure and unit costs. From movements and infrastructure parameters shipments and possible routes were generated for truck and multimodal option using transload facility. Unit costs parameters were used to formulate cost equations and to calculate transport and emission costs for both truck and multimodal alternatives, the outputs of the model. 22 Figure 11: Conceptual spreadsheet transport model diagram The input parameters for the spreadsheet are illustrated in Table 3 and the outputs in Table 4. 23 Table 3: Input parameters for developing conceptual spreadsheet transport model Input Category Input Parameters Movements Origin and Destination Volume of Commodities Infrastructure Unit Costs Road network Shipping costs Rail network Emission costs Potential transload locations Fuel Surcharges Transloading costs Table 4: Outputs from the conceptual spreadsheet transport model Outputs for Truck only transport Shipping cost to final destination Outputs Emission cost to final destination Multimodal transport Shipping cost for truck drayage to transload facility Shipping cost for rail to intermediate/final destination Shipping cost for truck drayage to final destination (if final destination does not have rail access) Emission cost for truck drayage to transload facility Emission cost for rail to intermediate/final destination Emission cost for truck drayage to final destination (if final destination does not have rail access) Examples of shipping and emission cost calculations that were later used to analyze the percent cost savings from multimodal transport from the three potential transload facility locations are provided in Appendix A-E. Examples of calculations of actual savings are shown in Appendix F and Appendix G. 24 3.3. Notation of Parameters for Equation Formulation The following sections describe the notations and the equations used in the calculations. The same parameters were used for both inbound and outbound freight, as they were assumed to take the same routes. General terms used in Notations ൌ , ൌ ൌ ൌ ൌ , ൌ m = multimodal (truck-rail or truck-rail-truck) $ = US dollars Volume of Commodities: ൌ ሺሻ Origin and Destination ݅ ൌ ሺ ൌ ͳǡ ʹǡ ͵ǡ ǥ Ǥ ǡͳͷሻ ݈ ൌ ሺ ǡ ǡ ǡ Ǥ ሻሾ݈ ൌ ͳǡ ʹǡ ͵ǡ Ͷሿ Distances from Road and Rail Network ௧ ܦǡ ൌ ሺሻ ௧ ܦǡ ൌ ሺሻሺሻ ܦǡ ൌ ሺሻ ܦǡ ሺሻ ௧ ൌ ሺሻሺሻ [if ܦǡ ௧ ൌ Ͳሿ final destination has rail access, ܦǡ 25 Potential Transload Location ݆ ൌ ሾ ൌ ͳǡ ʹǡ ͵ሿሺǡ ሻ Shipping Costs ݑ௧ ൌ ሺ̈́ െ ሻ ݑ ൌ ሺ̈́ െ ሻ Emission Costs ୣ୲ ൌ ሺ െ ሻ ୣ୰ ൌ ሺ െ ሻ ݑ ൌ ଶ ($ per ton of ଶ ) Fuel Surcharges ܨܤ௧ ൌ ሺ̈́ െ ሻ ൌ ሺ̈́ሻ ൌ ሺ̈́ሻ ܨܤ ൌ ሺ̈́ െ ሻ ൌ ሺ̈́ሻ ൌ ሺ̈́ሻ Transloading Cost ݄௧ ൌ ሺ̈́ሻ ൌ ሺ ൌ ͳǡ ൌ ʹሻ Total Costs ௧ ൌ ሺ̈́ሻ ൌ ሺ̈́ሻ ௧ ܥǡ ൌ ሺ̈́ሻ ௧ ܥܧǡ ൌ ሺ̈́ሻ ܥܧǡ ǡ ܥܧǡ ൌ ሺ̈́ሻ 26 ܥܧǡ ൌ ሺ̈́ሻ ௧ ൌ ሺ̈́ሻ ܥܦǡ ǡ ܴܥǡ ൌ ሺ̈́ሻ ܴܥǡ ௧ ܥܦǡ ൌ (if direct rail access at final destination= 0) ሺ̈́ሻ ǡ ܶܥǡ ൌ ሾ ǡ ܶܥǡ ൌ Ͳሿ ܶܥǡ ܥܨǡ ൌ ሺ̈́ሻ ௧ ൌ ሺ̈́ሻ ܥܨǡ ൌ ሺ̈́ሻ ܥܨǡ ௧ ௧ ǡ ܥܨǡ ൌ ܥܨǡ ௧ ሾ ǡ ܥܨǡ ൌ Ͳሿሺ̈́ሻ 3.4. Equation Formulation Equations were formulated using the parameters in Section 3.2. Truck and multimodal transport cost formulation is as follows: Shipping Costs For truck only mode, shipping cost from origin to final destination will be calculated using Equation 1. ௧ ௧ ܥǡ ൌ ൈ ܦǡ ൈ ݑ௧ (1) For multimodal option, truck drayage is required from origin to transload facility. Truck drayage from origin i to transload facility j will be calculated using Equation 2. ௧ ௧ ܥܦǡ ൌ ൈ ܦǡ ൈ ݑ௧ (2) Rail transport from transload j to intermediate k (without rail access) or final destination l (if rail access is available) will be calculated using Equation 3. 27 ǡ ܴܥǡ ൌ ൈ ሺܦǡ ܦǡ ሻ ൈ ݑ௧ ܴܥǡ (3) For final drayage in multimodal option (if needed), the cost of drayage will be calculated using Equation 4. ௧ ௧ ܥܦǡ ൌ ൈ ܦǡ ൈ ݑ௧ (4) Fuel Surcharges Fuel surcharge for truck only option will be calculated using Equation 5. ௧ ௧ ܥܨǡ ൌ ܸ ൈ ܦǡ ൈ ሺܨܤ௧ ൈ ሻ (5) Fuel surcharge for multimodal option will be calculated using Equation 6. ௧ ௧ ܥܨǡ ൌ ܥܨǡ ሺܥܨǡ ܥܨǡ ሻ ܥܨǡ (6) Where, ௧ ௧ ௧ ௧ ܥܨǡ ܥܨݎǡ ൌ ܸ ൈ ሺܦǡ ܦǡ ሻ ൈ ሺܨܤ௧ ൈ ሻ ܥܨǡ ൌ ܸ ൈ ሺܦǡ ܦǡ ሻ ൈ ሺܨܤ ൈ ሻ ܥܨǡ Transloading Cost Transloading cost for multimodal option will be calculated using Equation 7. ݉ ݉ ݆ܶܥǡ݇ ܶ݇ܥǡ݈ ൌ ܿ ݐ݄ݔܸݔ (7) Emission Costs For truck only option, Truck emission cost from origin to final destination will be calculated using Equation 8. A factor ͳͲି is multiplied to convert grams to tons. ௧ ௧ ܥܧǡ ൌ ൈ ܦǡ ൈ ୣ୲ ൈ ݑ ൈ ͳͲି (8) 28 Multimodal emission cost from origin to final destination will be calculated using Equation 9. ௧ ௧ ܥܧǡ ൌ ܥܧǡ ൫ܥܧǡ ܥܧǡ ൯ ܥܧǡ (9) ǡ ௧ ௧ ܥܧǡ ൌ ܸܦݔǡ ݔୣ୲ ݑݔ ିͲͳݔ ௧ ௧ ܥܧǡ ൌ ൈ ܦǡ ൈ ୣ୲ ൈ ݑ ൈ ͳͲି ሺif final destination has rail access= 0) ǡ ܥܧǡ ൌ ൈ ሺܦǡ ܦǡ ሻ ൈ ୣ୰ ൈ ݑ ൈ ͳͲି ܥܧǡ Total Costs Equations Total transport cost for truck only option will be determined using Equation 10. ݐൌ ݅ݐܥǡ݈ ݅ݐܥܨǡ݈ ݅ݐܥܧǡ݈ (10) Total transport cost for multimodal option will be calculated using Equation 11. ௧ ௧ ൌ ܥܦǡ ൫ܴܥǡ ܴܥǡ ൯ ܥܦǡ ܥܨǡ ܥܧǡ ܶܥǡ ܶܥǡ 29 (11) CHAPTER FOUR: TRANSLOAD FACILITY ANALYSIS FOR MICHIGAN’S UPPER PENINSULA 4.1. Introduction There are currently four railroads serving the Upper Peninsula of Michigan; one Class I railroad (CN) and three short line railroads – Escanaba & Lake Superior (E&LS), Lake Superior & Ishpeming (LS&I) and Mineral Range Railroad (MNRR). CN has the largest network (511 miles) within the UP and it controls all rail connections with the neighboring states and Canada. There is no direct rail connection between Upper and Lower Peninsula and the only direct road connection is the Mackinac Bridge which allows maximum truck weight of 72 tons (144,000 lbs). [74, 75] The rail network in the UP is illustrated in Figure 12. 30 31 Figure 12: Upper Peninsula rail and road network 31 Recently, several industry stakeholders and economic development agencies have expressed desire toward establishment of an intermodal/transload facility in the UP. Currently there is no intermodal facility in the UP. The nearest one is located in Chippewa Falls, Wisconsin and owned by CN. There is one private transload facility at Menominee (southern part of UP), owned and operated by KK Integrated Logistics, Inc. and served by both CN and E&LS Railroad. KK Integrated Logistics provides comprehensive logistics service, but have had challenges to make industries aware of their services. Escanaba and Lake Superior Railroad (E&LS) also has past experience in transloading. In the late 1980’s, E&LS purchased a 13,500 sq. ft. former lumber distribution center and warehouse in Kingsford, MI, and renovated it to serve as strategic truck/rail transload center for industry in the central UP. The research concentrated on evaluating the potential to establish a transload location at one of the three locations identified by stakeholders during shipper and railroad interviews (MDOT project) (Figure 13). The selection of these locations for analysis did not follow any analytical evaluation, but was solely based on input from shipping community and railroads. The following section provides a brief introduction of each location and the primary reasons behind the selection for analysis. It should be noted again that this research concentrated purely on evaluating the potential cost benefits for shipments and excluded any evaluations of capital, acquisition, utility, and other relevant costs for a transload facility development. 32 Figure 13: Potential transload facility locations Nestoria Nestoria is located along the Ishpeming –L’Anse rail line owned and operated by CN Railroad. The longitude and latitude of the location are 46.570 and -88.260 respectively. There is a lot of interest toward establishing a transload location in the vicinity of the industrial park in L’Anse/Baraga area, but rail service to L’Anse is extremely challenging due to extensive grades. Nestoria is a potential “compromise” location. It is past the high grades from L’Anse and the extensive swamps that surround tracks closer to L’Anse. In addition, it already hosts the Peshekee Yard by J.M Longyear and has a good road access from US-41. Ishpeming Ishpeming has an existing yard, owned and operated by CN Railroad (Figure 5). The longitude and latitude of the location are 46.500 and -87.690. The yard that has ample capacity, making it a prime location for establishing a transload facility. The yard is also an existing interchange point between railroads and Ishpeming / Marquette area is one of 33 the densest locations for economic activity in the UP, offering opportunities for concentrated shipments. Figure 14: Ishpeming rail yard owned by CN Railroad Amasa Amasa siding, known as Park Siding, is owned and operated by E&LS shortline Railroad. The longitude and latitude of the location are 46.390 and -88.530 respectively (Figure 6). Amasa is located further south than the other two locations, providing closer vicinity to industries in Iron county. Although there is currently no rail siding or yard available, a proper site with sufficient utilities and access has been identified and E&LS has a nearby yard in Channing with ample available capacity. 34 Figure 15: Amasa railsiding owned by E&LS Railroad 4.2. Modelling Scenarios The modeling effort concentrated on three objectives: x Calculation of shipping and emission costs for both truck and multimodal options for all three potential transload locations. (using spreadsheet method described in Chapter 3) x Comparison of percent cost savings of each alternative transload facility. x Identification of the impact of fuel price on the modal division (sensitivity analysis) x Comparison of carbon emission cost for the three potential transload locations Several scenarios were considered in calculations: x One with direct rail access to final destination and another with a 25 miles truck drayage to the final destination. x With and without considerations to emission costs. x Three different On-Highway Diesel Fuel (HDF) prices ($3, $5, and $6 per gallon) to observe the effect of fuel price on total transport cost. 35 4.3. Input Data Sources and Preparation Infrastructure, volume and cost data had to be developed for the analysis. The following sections explain the development and sources for key input parameters. 4.3.1. Infrastructure Infrastructure data were collected from the following sources x County shapefile for identifying county centroids was downloaded from Michigan Geographic Data Library. [76] x Road and rail networks were downloaded from Michigan Geographic Data Library. Rail network was also verified with railroad companies. 4.3.2. Shipments After it became evident that sufficient volume of freight data could not be collected for comprehensive analysis in the UP, a more limited approach, using TRANSEACH data was selected. TRANSEARCH is a unique planning tool that helps strategic transportation planners, transportation providers, and government agencies to analyze current and future freight flows by origin, destination, commodity, and transport mode. It is based on more than 100 sources including waybills, the Commodity Flow Survey, etc. The commodities are classified by “4-digit Standard Transportation Commodity Code” (STCC4) and origins and destinations are classified with Commodity Analysis Zone (CAZ). Commodity movements are measured in tons. The TRANSEARCH database had some inconsistencies with distances and those were removed from calculation. After removing the inconsistencies for distances, the freight volumes for origination and terminating interstate freight traffic were developed for the fifteen UP counties. All UP counties were selected for the analysis, although the counties nearest to proposed facilities were expected to see the greatest benefits. For this study interstate movement between the UP and other three out of state locations (Wisconsin, Chicago and Sault Ste. Marie) were analyzed. Shipments beyond these 36 locations were excluded, as they required an interchange between Class 1 railroads and it was not feasible to obtain proper shipping rates from public sources. All commodities included in the TRANSEARCH database, except secondary movements were in the analysis. Secondary traffic which includes movements between distribution stores was excluded from the research as they require door to door service were not considered as realistic candidates for multimodal movements. Table 5 summarizes the inbound and outbound interstate commodity flow by truck and rail in the UP. Commodities are categorized using “Standard Transport Commodity Code” (STCC). 37 Table 5: Interstate movement by truck and rail in the UP, 2009 Tons (2009) Commodities STCC Truck Inbound Rail Inbound Truck Outbound 1 268,608 Ͳ 38,218 Ͳ Iron Ores Nonmetallic Ores and Minerals 10 264,050 Ͳ 0 1,460,308 418,265 Ͳ 64,296 Ͳ Food Products Lumber and Wood Products 20 338,867 Ͳ 75,056 Ͳ 149,875 193,920 1,713,462 576,560 138,128 105,480 299,795 908,160 242,987 Ͳ 194,486 Ͳ 219,072 Ͳ 216,511 Ͳ 31,286 Ͳ 16,965 Ͳ 153,794 499,680 137,063 68,600 Agriculture Pulp and Paper Mill Products 14 24 Rail Outbound 26 Chemical Products Petroleum or Coal Products 28 Rubber and Plastics Clay, Cement, Glass, Stone Products 30 Primary Metal Products 33 105,414 67,920 54,732 38,080 Fabricated Metals 34 55,229 Ͳ 55,111 Ͳ Machines 35 31,294 Ͳ 34,814 Ͳ Waste or Scrap Material Secondary Traffic 40 50 Ͳ 2,800 49,200 550,150 Ͳ 81,441 Ͳ Other 222,241 306,928 364,271 4,080 Total 3,189,260 1,176,728 3,346,220 3,104,988 29 32 From Table 5, it is noted that the lumber and wood products; pulp and paper mill products; clay, cement, glass, stone products; and primary metal products; fabricated metals; and machines, are currently being hauled by both truck and rail. The selected commodities can be either handled as bulk or in containers. In this research, it was assumed that all movements from the transload facility would move in carloads, as development of a container facility would present a much more challenging scenario than that of a transload facility. The volumes were divided based on their origin/destination 38 county for analysis, as presented in Table 6 and they accounted for almost 50 percent of total interstate movements to/from UP. Table 6: TRANSEARCH volume for the UP counties for selected interstate movements Counties Alger Baraga Houghton Ontonagon Keewenaw Marquette Gogebic Dickinson Iron Delta Menominee Luce Chippewa Schoolcraft Mackinac Total Wisconsin Inbound Outbound 27,409 161,951 44,265 153,469 76,541 94,186 35,509 95,120 3,078 44,534 204,236 132,100 133,634 231,132 471,955 388,618 81,034 300,645 234,997 196,213 177,038 403,572 6,831 10,345 40,690 15,162 15,309 24,354 17,332 51,426 1,569,858 2,302,827 Chicago Inbound Outbound 2,910 16,132 7,644 2,692 9,319 3,011 1,962 441 253 0 28,897 1,911 7,073 3,088 24,353 26,131 4,493 3,141 27,034 18,446 22,272 17,820 732 3,331 6,826 20,094 1,334 231 1,563 44,921 146,665 161,390 39 Minneapolis SaultSte.Marie Inbound Outbound Inbound Outbound 2,406 1,487 3,017 109 6,070 18,416 21,917 434 18,260 22,162 14,018 735 6,178 90,076 0 0 492 1,187 0 0 301,137 15,223 0 1,069 28,248 39,095 0 3,027 18,988 31,775 9,361 9,033 4,729 20,778 25,966 233 20,552 6,908 8,000 13,973 25,411 412 309 1,492 88 15,000 200 3,786 1,272 1,500 550 983 163 2,100 321 1,082 129 1,221 428,376 274,170 93,291 25,241 One of the greatest limitations to the analysis was the determination of shipping distances. TRANSEARCH 2009 database offers origin/destinations at county level within the UP and state level outside the State of Michigan. Centroids of counties (and states) were used as the origin/destination data. It is recognized that this assumption significantly hinders the accuracy of analysis, but researchers could not identify other alternatives. Out of state origins and destinations are illustrated in (Figure 16). The distance calculations included the following: x Distance from centroid of counties to transload facility x Distance from transload facility to final destinations o Wisconsin - Bradley o Chicago – Kirk yard o Minneapolis - Withrow o Sault Ste. Marie in Canada – Soo yard Distance from transload transload facility to final destination (Bradley, Kirk Yard, Withrow) are selected relative to the centroid of the respective states. 40 Figure 16: Out of state origins and destinations (CN Website) County shapefile was imported into ArcGIS to locate centroids of the counties. The longitudes / latitudes of the centroids were determined using the “Polygon to Points” feature built in ArcGIS 10.1 version and further used to calculate the shortest road distance from centroids to transload locations and to county centroids of the selected destinations (Wisconsin, Chicago, Minneapolis and Sault Ste. Marie). For determining the rail distances, the research used the Mineral Occurrence Estimation and Visualization (MOREV) tool, based on ArcGIS and developed by Michigan Tech Research Institute (MTRI). 4.3.3. Costs Parameters Truck Rates Based on literature review on trucking rates, they vary greatly between $0.03 and $0.19 per ton-mile with a median of $0.1 and the average payload used for trucks is between 14 41 to 25 tons. Total weight of a Wisconsin/ Minnesota trucks are 40 tons (80,000 lbs), whereas Michigan trucks are 82 tons (164,000 lbs).[10, 43] Therefore, the payload for Wisconsin/ Minnesota truck is 25 tons, whereas for Michigan it is 50 tons. As Wisconsin/ Minnesota trucks have less carrying capacity, their rates per ton tend to be higher than Michigan trucks to overcome the operating cost. The truck rates for the UP case study used Wisconsin and Minnesota truck rates, as all movements originate or terminate beyond the state of Michigan. Hicks study (2009) conducted on WI/MN log trucks, found the rate to be $0.20 (converted to 2012 index) per ton-mile excluding the fuel surcharge. For the UP case study, three shippers and two truck companies in Michigan were contacted for the rate and they informed the truck unit shipping cost is in between “$2.50 to $3.50 per mile”. The rate was converted per ton mile for 25 ton payload truck, resulting in an average value $0.15 per ton-mile. Rail rates Based on the literature review on rail rates, they vary greatly between $0.01 and $0.04 per ton-mile. Instead of using historic rates, the study looked into the operating Class I CN Railroad’s tariff rates in the study area which are publicly available. Any potential discounts to shippers by CN were ignored in the research. Rail rates for this study are comprised of CN tariff rate and CN 7403 rule (mileage based) for fuel surcharge.[77, 78] These two values were combined to estimate the rail shipping cost for the research and CN tariff rates for fifty random origins and destinations were chosen to generate an equation for rail rates.[79] Each origin and destination was used to generate price for two different types of carloads (100 and 70 tons capacity) that are standard sizes for CN. [77] The distance between each origin and destination was found using MOREV tool. The carload price was converted to US dollar per ton-mile by dividing the price with volume (100 and 70 tons) and distance (MOREV). Initially, the cost per ton-mile was calculated separately for different commodities, until it was recognized that the unit cost was the same across commodity types. After calculating cost per ton-mile for each origin and destination pair, the values were plotted on a graph to match them with an equation that 42 resembles the rates (y = 2.5492x-0.584). (Figure 17).The R-squared value was found as 0.9572 which exhibits a good correlation between the cost per ton-mile and distances. Cost per ton-mile vs Distance (CN Tariff Rate) 0.50 0.45 Cost per ton-mile 0.40 0.35 0.30 0.25 0.20 y=2.5492xͲ0.584 R²=0.9572 0.15 0.10 0.05 0.00 0 200 400 600 800 1,000 1,200 1,400 Distance, miles Figure 17: Cost per ton-mile vs distances from CN Tariff rate When shipment requires an interchange between two railroads such as CN with E&LS or vice versa, there is an additional shipping cost known as interchange fee. The interchange cost varies for different railroads and is not publicly available. The study contacted E&LS railroad to get some values for interchange, but due to confidentiality they could not be provided. Hence, the study assumed the CN tariff rates for calculating rail shipping cost on E&LS lines without including interchange cost. 43 Transloading Cost For single mode movements, it is assumed that shipper and receiver are responsible for loading/unloading activities and no separate charge is applied. In multimodal transportation, transloading adds one or two loading/unloading cycles, depending on whether truck drayage is required in one or both ends. Based on expert interviews and past literature, $6 per ton was applied as additional loading/ unloading cost for each transload between truck/rail. Fuel Surcharges for rail and truck Fuel surcharges are imposed on rail and truck as per mile basis in addition to fuel price. For trucks, current available online tariff rates were used to determine the base fuel surcharge and findings by Hicks was used to determine the increase in fuel surcharge for every dollar increase in fuel price. CN tariff rates (Rule 7403, mileage based fuel surcharge) were used for calculating fuel surcharges rail along with the increase in fuel surcharge for every dollar increase in fuel price.[43, 78] The base price for On-Highway Diesel Fuel (HDF) was assumed different for truck (2014) and rail (2008). The base price is provided by Energy Information Administration and it was $3.989 per gallon in February 2014 for On-Highway Diesel Fuel (HDF). From the current tariff rates available online, the base fuel surcharge for truck is approx. $0.65 per mile [80, 81] and assuming payload for truck as 25 tons, the base fuel surcharge converted to $0.0256 per ton-mile. From Hicks’ study, fuel surcharge for truck increases $0.014 per ton-mile for every $1 increase in HDF price. Equation presented in Chapter 3 was used to calculate truck fuel surcharges. For rail, the base rate of HDF was given as $2.30 in CN tariff rate and fuel surcharges in February 2014 was $0.003180 per ton-mile. Fuel surcharge for rail increases $0.002 per ton-mile for every $1 increase in HDF price (provided in CN tariff rate). 44 Emissions factors and costs From past studies the range of emission factors for rail was found to be 15.40 to 41.42 grams per ton-mile with a median of 24.39. CN uses 28.73 grams per ton-mile as emission factor for rail and 183.54 grams per ton-mile for trucks in their carbon calculator. [70] CN also generated truck emission factor from the federal regulations limits which are applicable for both U.S. and Canadian trucks. It was decided that the average between the median value of past studies and CN values should be used for generating emission factors for rail and truck for the UP case study; 26.50 and 160 grams per ton-mile, respectively. The monetary value of emissions from past studies was found in between $2.27 to $36 per ton of CO2 emission. $2.27 was considered as an outlier and an average of the remaining range; $30.5 was selected for the case study. Forkenbrock used his cost values for rural areas in 1994. However, the recent studies in 2012 presented the values which would be applicable for both rural and urban areas. 4.4. UP Case Study results The following sections describe the outcomes of the calculations and complimentary analysis. The various costs obtained from the calculations spreadsheet were used to further evaluate percent cost savings from multimodal options with various fuel prices, excluding and including emission costs. 4.4.1. Modeling Outcomes for County Based Analysis For movements from various counties, it was recognized that only Minneapolis and Chicago movements could obtain cost savings from multimodal transportation. The counties surrounding the transload facilities could achieve 2 to 25 percent benefits for Chicago movements with direct rail access at final destination. (Figure 18) In the figure, negative benefits indicate an increase in transport cost with multimodal transport option and positive benefits indicated a decrease in transport cost. For Chicago movements (Figure 19), seven counties would obtain benefits for a transload facility in Amasa, whereas six counties would gain benefits for Nestoria and Ishpeming. (Figure 20). 45 ChicagoMovements(withdirectrailaccessatfinaldestination) Mackinac Schoolcraft Chippewa Luce Menominee Delta Iron Dickinson Gogebic Marquette Keweenaw Ontonagon Houghton Baraga Alger Ͳ50 Ͳ40 Ͳ30 Ͳ20 Ͳ10 0 10 20 30 Percentcostsavings,% Amasa Ishpeming Nestoria Figure 18: Percent cost savings for Chicago movements (with direct rail access at final destinations) 46 Figure 19: Benefitted counties for Chicago movements 47 Minneapolis movements were also analyzed for the percent cost savings. With direct rail access, Amasa would provide benefit 2 to 25 percent benefits for twelve counties, whereas Nestoria and Ishpeming would provide benefits for ten counties each. 48 MinneapolisMovements(withdirectrailaccessatfinaldestination) Mackinac Schoolcraft Chippewa Luce Menominee Delta Iron Dickinson Gogebic Marquette Keweenaw Ontonagon Houghton Baraga Alger Ͳ40 Ͳ30 Ͳ20 Ͳ10 0 10 20 30 Percentcostsavings,% Amasa Ishpeming Nestoria Figure 20: Percent cost savings for Minneapolis movements (with direct rail access at final destinations) 49 Figure 21: Benefitted counties for Minneapolis movements 50 If 25 mile truck drayage was added to reach the final destination, no benefit could be obtained for Chicago and Minneapolis movements. (Appendix J and Appendix K) 4.4.2. Effect of Emission Costs When emission costs were considered, the potential benefits would increase by 1 to 2 percent (Appendix L and Appendix M ) The emission cost was found as low because the emission cost calculated based on per ton of CO2 emission. When the amount of CO2 were calculated it was found significantly low when converted to ton of CO2. 4.4.3. Transload facility location comparison The analysis revealed that Amasa would had the highest potential volume for multimodal alternative. In Figure 22, percent cost savings for individual interstate movements are presented (excluding and including emission), using HDF price $3.898 per gallon (February 2014). For Chicago movements, Nestoria would offer the greatest savings with 22 and 24 percent (with and without emission costs). For Minneapolis movements, Ishpeming would be preferred, providing 20 and 22 percent savings. 51 PercentCostSavingsforeachTransloadFacilityfor IndividualInterstateMovements ExcludingEmission IncludingEmission Percentcostsavings,percent 25 20 15 10 5 0 Chicago Minneapolis Nestoria Chicago Minneapolis Ishpeming Chicago Minneapolis Amasa Figure 22: Percent cost savings from each transload facility for Chicago and Minneapolis movements (excluding and including emission) The total percent cost savings (weighted average) for Chicago and Minneapolis movements (excluding and including emission) are summarized in Figure 23. Ishpeming had the greatest overall potential for savings. 52 Total Percent Cost Savings from each Transload Facility ExcludingEmission IncludingEmission TotalPercentcostsavings,percent 20 18 16 14 12 10 8 6 4 2 0 Nestoria Ishpeming Amasa Figure 23: Total percent cost savings from each transload facility for interstate movements (Chicago and Minneapolis) 4.4.3.1. Sensitivity Analysis of Transload Facilities for Different HDF Prices Three scenarios were created to analyze the variation of percent cost savings with different fuel prices for both truck and multimodal transport options. The sensitivity analysis was conducted for HDF price $3, $5 and $6 per gallon. Figure 24 presents sensitivity analysis for Chicago movements with direct rail access at final destinations and Figure 26 for Minneapolis. Nestoria shows dominance over other two facilities for Chicago movements and Ishpeming for Minneapolis movements, in terms of percent cost savings. For every $1 increase in fuel prices, percent cost savings from multimodal option for such movements increased by approx. 3.5 to 4.5 percent. 53 Base Scenario Figure 24: Sensitivity analysis of percent cost savings from each transload facility for different HDF prices for Chicago movements 54 Base Scenario Figure 25: Sensitivity analysis of percent cost savings from each transload facility for different HDF prices for Minneapolis movements 4.5. x Findings from the UP Case Study Based on the cost parameters used for shipping, emission, handling and fuel surcharge costs for truck and rail, only Chicago and Minneapolis movements with direct rail access would gain benefits from transload facilities. x Ishpeming would be preferred for Minneapolis movements. On the other hand, Nestoria would be preferred for Chicago movements. (Figure 22) x Ishpeming was found better for maximum overall percent cost savings because of its lowest distance from the benefitted counties. x From volume perspective Amasa would be better and from cost savings perspective Ishpeming would be better. 55 x If emission cost is included, an additional 1 to 2 percent can be saved for using transload facility. (Appendix L and Appendix M) x For $1 increase in HDF prices, percent cost savings would increase 3 to 5 percent for using multimodal option. (Figure 24 and Figure 25) x Amasa would provide potential benefits to maximum number of counties for both Chicago and Minneapolis movements. (Figure 19 and Figure 21) 56 CHAPTER FIVE: CASE STUDIES FOR TWO MICHIGAN UP COMPANIES 5.1. Introduction Due to lack of accuracy on TRANSEARCH data, the analysis was expanded into two specific case studies, DA Glass America and Northern Hardwoods, who were able to provice more specific data parameters. Even with case studies, the research did not look into the movements beyond Chicago and Minneapolis due to lack of information on rail shipping and interchange cost. Figure 26 illustrates the location of two companies, as it related to the three potential transload locations in the UP. Figure 26: Case study companies' locations 57 5.2. DA Glass America DA Glass America is located Near the Houghton County Memorial Airport, between Hancock and Calumet, Michigan. The company is planning to start operations in early 2014. The company will employ 30 employees to produce anti-reflective glass and process sheet glass for Greenhouses. Their main shipping destinations include Wisconsin; southwestern USA; and California and they are very interested in multimodal opportunities. Shipments would use containers filled by their glass products (24 tons each). They expect approximately 1,200 outbound and inbound container movements to Carlton, Wisconsin. The study was going to compare truck option with multimodal alternative for their container shipments to Wisconsin, but it was found that the short distance and volume would make it extremely unlikely scenario for intermodal movements, even if an intermodal facility was already existing. For this reason, the analysis was abandoned. 5.3. Northern Hardwoods Northern Hardwoods is located in South Range, MI. They manufacture lumber products and are currently shipping around 70 percent of their volume to Wisconsin (New London, De Pere, Theresa and Mercer) and the rest to Minneapolis (St. Cloud and Maple Grove) using flatbed cars (used for lumber transport). Some of the Minneapolis movements are further transloaded to trains for more distant destinations. Northern Hardwoods is also interested in export opportunities to Asia, but lack of multimodal opportunities is impeding the development of global business. This study compared their truck option with multimodal for their shipments to Minneapolis and locations in Wisconsin. 58 5.3.1. Development of Parameters for Northern Hardwoods Rail Rates Northern hardwoods would use centerbeam cars for their rail shipments, so their rail rates used the ones based on CN tariff rates, as presented in Chapter 4: Input Data Sources and Preparation. Truck Rates In the UP case study, the unit cost for interstate truck movements was $0.15 per ton-mile. For these movements, the study used the flat rate of $0.15 per ton-mile. If the company shipped their products to a transload facility in the UP, they would be using Michigan trucks with higher carrying capacity. Using Michigan trucks would give them the opportunity to lower their operating cost per ton-mile by 50%, leading into $0.075 per ton-mile as the rate for the drayage to the potential transload location. Fuel Surcharges / Transloading / Emission Costs Values for fuel surcharges, transloading and emission costs used value and formulas presented in Chapter 4 : Costs Parameters. 5.3.2. Study results for Northern Hardwoods For Northern Hardwoods, the distances for truck and multimodal for Wisconsin movements are presented in Figure 27. For Wisconsin, there are 4 destinations. The distances for the destinations were taken as weighted average. 59 Figure 27: Distances for Northern Hardwoods for truck only and multimodal option with rail access in final destination (Wisconsin movements) Figure 28 summarizes multimodal cost savings over truck option for Wisconsin movements, with and without emission costs. The analyses were done with and without direct rail access (25 miles) in the final destination. This also included sensitivity analysis for different HDF prices. The analysis found that without direct access by rail at final destination, no benefits could be gained for Wisconsin movements, mainly due to short overall distances. All the locations provided potential benefits, but Amasa would be the preferred location (approx. 8 to 20 percent) for current and higher fuel prices. 60 Figure 28: Multimodal cost savings for Northern Hardwoods (Wisconsin movements) using transload facility For Northern Hardwoods, the distances for truck and multimodal for Minneapolis movements (weighted average distance for St. Cloud and Maple Grove) are presented in Figure 29. For truck only option, the distance was 360 miles. Amasa transload facility offered the shortest total multimodal distance (350 miles). 61 Figure 29: Distances for Northern Hardwoods for truck only and multimodal option (Minneapolis movements) Figure 30 summarizes multimodal cost savings over the truck option for Minneapolis movements for Northern Hardwoods. The analyses were done with and without direct rail access (25 and 50 miles final truck drayage) in the final destination. This also included sensitivity analysis for different HDF prices. All transload locations would provide benefits for current and higher HDF prices, but Amasa would be the preferred location. Movements to Minneapolis have potential to gain benefits, even if 25 or 50 miles drayage were required to reach the final destination. 62 Figure 30: Multimodal cost savings for Northern Hardwoods (Minneapolis movements) using transload facility 63 CHAPTER SIX: CONCLUSIONS AND FUTURE RESEARCH 6.1. Conclusions The objective of this research was to investigate whether a combination of two or more modes of transportation, also known as intermodal or multimodal transport, would provide any potential cost savings to the shippers in the Upper Peninsula of Michigan. The research developed a calculation methodology to evaluate the potential savings with and without consideration for emission costs. The analysis included sensitivity analysis of such savings to changing in fuel prices. The research used three potential locations, Nestoria, Amasa and Ishpeming for transload facilities. The selection of locations was based purely on shipper and railroad input. TRANSEARCH 2009 database was used to evaluate the savings potential at the county level and a specific case study of Northern Hardwoods to do the same at the company level. Unfortunately, the analysis was limited to movements to/from Wisconsin, Chicago and Minneapolis, as rail rate information shipment beyond these locations could not be secured. The calculation results showed potential cost benefits from multimodal alternatives for the counties that surrounded the potential transload facilities. The savings were present from all three potential transload locations, but could only be realized for Chicago and Minneapolis movements and only, if there was rail access to final destination, eliminating the need for final truck drayage. For Northern Hardwoods, movements to most distant locations in Wisconsin would also provide cost savings, partially due to the possibility of using Michigan trucks with higher carrying capacity for the initial movement from the facility to transload location. In addition, Minneapolis movements were found to provide savings even without final rail access. Sensitivity analysis was conducted for different On-Highway Diesel Fuel (HDF) prices and for every dollar increase in HDF price it was found that percent cost savings for multimodal option increased by 3 to 5 percent. 64 Emission costs, on the other hand, were quite insignificant adding only 0.5 to 2 percent in cost savings. While the study showed some potential for savings from multimodal freight options, it is recognized that the effort was greatly hampered by insufficient data. Especially the limitation of origin/destination data accuracy made analysis results questionable and the omission of trips that go beyond Chicago and Minneapolis removed the trips with greatest potential for multimodal options from the research pool. 6.2. Future Research For future work the following points should be considered x There is a need for a better understanding of freight movements in the UP. x If detailed freight shipment data could be secured, a study could be conducted to identify the optimal location for a transload facility, including the potential volumes. x Rail interchange cost should be understood to allow the inclusion of the most potential trips for multimodal alternative. x This analysis concentrated purely on shipping costs. For a more comprehensive analysis of potential transload facility establishment, capital and maintenance costs and long term cost-benefit analysis, should be included to understand the sustainability of such development. 65 References 1. Coyle, J., et al., Supply chain management: a logistics perspective. 2008: Cengage Learning. 2. Administration, U.S.E.I., U.S. Diesel and Crude Oil Prices. 2010 - 2015: U.S. Department of Energy. 3. Systematics, C., Freight—Rail Bottom Line Report. American Association of State Highway and Transportation Officials. AASHTO, Washington, DC, 2003. 4. Mentzer, J.T., et al., Defining supply chain management. Journal of Business logistics, 2001. 22(2): p. 1-25. 5. Christopher, M., Logistics and supply chain management. 2012: Pearson UK. 6. Management, C.o.L., Definition of Logistics. 1991. 7. Tilanus, B., Information systems in logistics and transportation. 1997: Pergamon. 8. Tseng, Y.-y., W.L. Yue, and M.A. Taylor. The role of transportation in logistics chain. 2005. Eastern Asia Society for Transportation Studies. 9. Systematics, C., Freight Transportation Modal Shares: Scenarios for a LowCarbon Future, U.S.D.o. Energy, Editor. 2013. p. 8. 10. Harwood, D.W., Review of truck characteristics as factors in roadway design. Vol. 505. 2003: Transportation Research Board. 11. Association, A.F.P. Truck Weight. 2014; Available from: http://www.afandpa.org/issues/truck-weights. 12. Angeles, T.P.o.L. Heavy Container Corridor. 2014; Available from: http://www.portoflosangeles.org/maritime/heavy_container.asp. 13. Transportation, U.S.D.o., Hours of Service of Drivers, F.M.C.S. Administration, Editor. 2014. 14. Tomlinson, J., History and impact of the intermodal shipping container. Pratt Institute, New York, USA, 2009. 15. Levinson, M., The box: how the shipping container made the world smaller and the world economy bigger. 2010: Princeton University Press. 16. Mclean, M.P., Apparatus for shipping freight. 1958, Google Patents. 66 17. Cheng, Y., The method to select the transport path based on the multimodal cost. Transport, 2012. 27(2): p. 143-148. 18. ECE, U., Terminology on Combined Transport. . United Nations (UN) & Economic Commission for Europe (ECE), New York and Geneva., 2001. 19. Railroads, A.o.A., Rail Intermodal Keeps America Moving. 2013. 20. Bektas, T. and T. Crainic, A brief overview of intermodal transportation. 2007: CIRRELT. 21. Rodrigue, J.P. THE GEOGRAPHY OF TRANSPORT SYSTEMS. 2014 [cited 2014; Available from: http://people.hofstra.edu/geotrans/eng/ch3en/conc3en/usrail18402003.html. 22. Balley., A. A new Era for North American Rail Intermodal. 2014 [cited 2014; Available from: http://www.du.edu/transportation/media/documents/pressreleases/2013-11-25-The-State-of-Rail-Intermodal.pdf. 23. Craig, A.J., E.E. Blanco, and C.G. Caplice, Carbon Footprint of Supply Chains: A Scoping Study. 2013. 24. Macharis, C., et al., A decision support framework for intermodal transport policy. EuropeanTransport Research Review, 2011: p. 167-178. 25. MOCUğA, G.E. and E. GHITA, THE ANALYSIS OF THE TRANSSHIPMENT CAPACITY OF AN INTERMODAL TERMINAL. 26. Steele, C.W., Freight Facility Location Selection: A Guide for Public Officials. 2011: Transportation Research Board. 27. Middendorf, D., Intermodal terminals database: concepts, design, implementation, and maintenance. Center for Transportation Analysis, 1998. 28. Notebooks, T.M.R. Typical Diagram of Transload Facility. 2014; Available from: http://modelrailroadersnotebook.blogspot.com/2012/04/007-utah-night-shiftindustries.html. 29. Thomson, D.M. Transloads: Freight Movement Efficiencies in the Next Decade. in 2012 Joint Rail Conference. 2012. American Society of Mechanical Engineers. 30. HDR Engineering, I., Geiger Spur Transload Facility Study, W.S.D.o. Transportation, Editor. 2007. 67 31. Stewart, R.D., et al., Evaluating Export Container Pooling Options in MN, WI, and MI's Upper Peninsula. 2013. 32. McGinnis, M.A., A comparative evaluation of freight transportation choice models. Transportation Journal, 1989. 29(2): p. 36-46. 33. Korpela, J. and M. Tuominen, A decision aid in warehouse site selection. International Journal of Production Economics, 1996. 45(1): p. 169-180. 34. Kozan, E. and S.Q. Liu, A demand-responsive decision support system for coal transportation. Decision Support Systems, 2012. 54(1): p. 665-680. 35. Ji, P., K.J. Chen, and Q.P. Yan, A Mathematical Model for a Multi-Commodity, Two-Stage Transportation and Inventory Problem. International Journal of Industrial Engineering, 2008. 36. Rich, J., P.M. Holmblad, and C.O. Hansen, A weighted logit freight mode-choice model. Transportation Research Part E: Logistics and Transportation Review, 2009. 45(6): p. 1006-1019. 37. Lee, H., et al., Designing an integrated logistics network in a supply chain system. KSCE Journal of Civil Engineering, 2013. 17(4): p. 806-814. 38. Mishra, S.S., An Integrated Framework for Modeling Freight Mode and Route Choice. 2013. 39. Chen, C.-T., C.-T. Lin, and S.-F. Huang, A fuzzy approach for supplier evaluation and selection in supply chain management. International journal of production economics, 2006. 102(2): p. 289-301. 40. Zionts, S. and J. Wallenius, An interactive programming method for solving the multiple criteria problem. Management science, 1976. 22(6): p. 652-663. 41. Hwang, C.L. and A.S.M. Masud, Multiple objective decision making-methods and applications. Vol. 164. 1979: Springer. 42. Ulungu, E. and J. Teghem, MultiǦobjective combinatorial optimization problems: A survey. Journal of MultiǦCriteria Decision Analysis, 1994. 3(2): p. 83-104. 43. Hicks, J.W., Modeling the Multi-modal Transport of Logs and Effects of Changing Fuel Prices. 2009, Michigan Technological University. 68 44. Arnold, P., D. Peeters, and I. Thomas, Modelling a rail/road intermodal transportation system. Transportation Research Part E: Logistics and Transportation Review, 2004. 40(3): p. 255-270. 45. Owens, T.D., D.P. Seedah, and R. Harrison, Modeling Rail Operating Costs for Multimodal Corridor Planning. Transportation Research Record: Journal of the Transportation Research Board, 2013. 2374(1): p. 93-101. 46. Winebrake, J.J., et al., Intermodal Freight Transport in the Great Lakes: Development and Application of a Great Lakes Geographic Intermodal Freight Transport Model. Rochester, NY, Great Lakes Maritime Research Institute, 2008. 47. Li, H., et al., Minimum-cost optimization in multicommodity logistic chain network, in Computer Algebra and Geometric Algebra with Applications. 2005, Springer. p. 97-104. 48. Officials, A.A.o.S.H.a.T., Freight–Rail Bottom Line Report. 2002. p. 25 - 35. 49. Hanssen, T.-E.S., T.A. Mathisen, and F. Jørgensen, Generalized Transport Costs in Intermodal Freight Transport. Procedia - Social and Behavioral Sciences, 2012. 54: p. 189-200. 50. Janic, M., Modelling the full costs of an intermodal and road freight transport network. Transportation Research Part D: Transport and Environment, 2007. 12(1): p. 33-44. 51. Forkenbrock, D.J., Comparison of external costs of rail and truck freight transportation. Transportation Research Part A: Policy and Practice, 2001. 35(4): p. 321-337. 52. Wang, C.-H., J.C. Even Jr, and S.K. Adams, A mixed-integer linear model for optimal processing and transport of secondary materials. Resources, conservation and recycling, 1995. 15(1): p. 65-78. 53. Administration, U.S.D.o.T.R.a.I.T. and B.o.T. Statistics. National Transportation Statistics. Available from: http://www.bts.gov/publications/national_transportation_statistics/. 54. Kehoe, O., Economics of Truck and Rail Freight Transportation. Economics. 12: p. 8-2003. 69 55. Associates, N.E.R., External Costs of Electric Utility Resource Selection in Nevada. 1993: Nevada Power Company. Cambridge, MA. 56. Crossing, C.R., Feasibility of Diverting Truck Freight to Rail in the Columbia River Corridor. 2006. 57. Atkinson, G. and S. Mourato, Cost-benefit analysis and the environment: recent developments. 2006. 58. Beresford, A., S. Pettit, and Y. Liu, Multimodal supply chains: iron ore from Australia to China. Supply Chain Management: An International Journal, 2011. 16(1): p. 32-42. 59. Corporation, R.T. Terminal Tariff 2014; Available from: http://www.rukert.com/docs/tariff.pdf. 60. Energy, U.S.D.o. and E.I. Administration, Transportation sector energy consumption in Annual Energy Review (Washington DC: Annual Issues), tables 2.1a, 2-1e, and 5-13c,. November, 2012. 61. Greene, D.L. and S. Plotkin, Reducing greenhouse gas emission from US transportation. Arlington: Pew Center on Global Climate Change, 2011. 62. Iden, M., Engines of Change & Future Fuels for US Freight Locomotives, in Faster Freight Cleaner Air 2008 conference. 2008. 63. Tolliver, D., P. Lu, and D. Benson, Comparing rail fuel efficiency with truck and waterway. Transportation Research Part D: Transport and Environment, 2013. 24: p. 69-75. 64. International, I., Comparative Evaluation of Rail and Truck Fuel Efficiency on Competitive Corridors. 2009. p. 5 - 12. 65. O'Rourke, L., K. Read, and E. Johnston. US Freight Emissions Segmented by BCO Industry. in Transportation Research Board 92nd Annual Meeting. 2013. 66. Hanaoka, S. and M.B. Regmi, Promoting intermodal freight transport through the development of dry ports in Asia: An environmental perspective. IATSS Research, 2011. 35(1): p. 16-23. 70 67. WATERWAYS, C.F.P.A. and T.T. INSTITUTE, A MODAL COMPARISON OF DOMESTIC FREIGHT TRANSPORTATION EFFECTS ON THE GENERAL PUBLIC. 2007. 68. Cefic and ECTA, "Guidelines for Measuring and Managing CO2 Emission from Freight Transport Operations". 2011. 69. BLANCO, E.E., CASE STUDIES IN CARBON-EFFICIENT LOGISTICS- Ocean Spray - Leveraging Distribution Network Redesign. 2013. 70. Railroad, C. CN Greenhouse Gas Emission Caluculator. 2007; Available from: https://www.cn.ca/en/repository/popups/ghg/ghgcalculatoremissionfactors. 71. Chernick, P.L. and E.J. Caverhill, The valuation of environmental externalities in energy conservation planning. Energy efficiency and the environment: forging the link. Washington, DC: American Council for an Energy-Efficient Economy, 1991: p. 215-228. 72. (NRC), N.R.C., Rethinking the Ozone Problem in Urban and Regional Air Pollution. 1991: Committee on Tropospheric Ozone Formation and Measurement. Washington, DC: National Academy Press. 73. Stastna, K., U.S. ups 'social cost' of carbon emissions, in CBC News. 2013. 74. Lautala, P., et al., Kinross Michigan Facility Biomass Transportation Systems Evaluation. 2012. 75. Authority, M.B. Mackinac Bridge Authority: Rules and Regulations. 2000; Available from: http://www.mackinacbridge.org/rules--regulations-32/. 76. Michigan, S.o. Michigan Geographic Data Library. 2002; Available from: http://www.mcgi.state.mi.us/mgdl/?rel=ext&action=sext. 77. Railroad, C. CN Tariff Rate. 2013- 2014; Available from: http://www.cn.ca/en/our-business/prices-tariffs-transit-times. 78. Railroad, C. Fuel Surcharge Calculation. 2014; Available from: http://www.cn.ca/en/customer-centre/tools/fuel-surcharge. 79. Railroad, C. CN Rail Capacity Map. 2013 [cited 2013; Available from: https://www.cn.ca/-/media/Images/Maps/en_RailCapacityMap.pdf. 71 80. Supply, D.C. Current Fuel Surcharge Rate. 2014; Available from: http://www.dogwoodceramics.com/fuel/current-fuel-surcharge.htm. 81. Services, P.T. Truck Fuel Surcharges. 2014; Available from: http://www.progressive-ord.com/transportation-surcharges/truck-fuel-surcharges/. 72 Appendix A: Example of calculating transport cost for truck drayage to transload facility (multimodal) County/Township oforigin Alger Baraga Houghton Ontonagon Keweenaw Marquette Gogebic Dickinson Iron Delta Menominee Outboundfor Volume Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin 161,951 153,469 94,186 95,120 44,534 132,100 231,132 388,618 300,645 196,213 403,572 Weighted Fuel Emission(ton Distanceto EmissionCost UnitShippingCost ShippingCost Distance Surcharge ofCO2) transloadfacility 85 0.15 2064875.25 2202.5336 67177.2748 30 0.15 690610.5 736.6512 22467.8616 60 0.15 847674 904.1856 27577.6608 75 0.15 1070100 1141.44 34813.92 100 0.15 668010 712.544 21732.592 40 0.15 792600 845.44 25785.92 83.91194573 4729228.928 95 0.15 3293631 3513.2064 107152.7952 70 0.15 4080489 4352.5216 132751.9088 65 0.15 2931288.75 3126.708 95364.594 95 0.15 2796035.25 2982.4376 90964.3468 140 0.15 8475012 9040.0128 275720.3904 27710325.75 29557.6808 901509.2644 Appendix B: Example of calculating rail transport cost for transporting out of state (multimodal) TransloadFacility Nestoria Distancefrom Emission(tons transloadfacility UnitShippingCost ShippingCost EmissionCost FuelSurcharge ofCo2) tooutofstate Outboundfor Volume Wisconsin 2,201,540 220 0.1093 52914942.15 12834.9782 391466.8351 1540197.384 Appendix C: Example of calculating final truck drayage cost (multimodal) Volume DistancetoFinal destination UnitShipping Cost ShippingCost 2,201,540 25 0.15 8255775 73 Emission(tonof EmissionCost CO2) 8806.16 268587.88 Loading/ Unloading FuelSurcharge Cost 13209240 1408985.6 Appendix D: Example of calculating transport cost for "truck only" transport County/Township Outboundfor oforigin Alger Wisconsin 84 161,951 Distanceof transport 230 0.15 Emission(ton EmissionCost ofCO2) 5587309.5 5959.7968 181773.8024 Baraga Wisconsin 153,469 150 0.15 3453052.5 3683.256 112339.308 Houghton Wisconsin 94,186 170 0.15 2401743 2561.8592 78136.7056 Ontonagon Wisconsin 95,120 130 0.15 1854840 1978.496 60344.128 Keweenaw Wisconsin 44,534 210 0.15 1402821 1496.3424 45638.4432 Marquette Wisconsin 132,100 180 0.15 3566700 3804.48 Gogebic Dickinson Iron Delta Menominee Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin 231,132 388,618 300,645 196,213 403,572 125 130 100 200 125 0.15 0.15 0.15 0.15 0.15 4333725 7578051 4509675 5886390 7566975 48141282 Volume UnitShippingCost ShippingCost 74 Weighted Distance 116036.64 145.7806263 4622.64 140990.52 8083.2544 246539.2592 4810.32 146714.76 6278.816 191503.888 8071.44 246178.92 51350.7008 1566196.374 Fuel Surcharge 8216112.128 Appendix E: Example of summary of "truck only" and multimodal transport cost Drayagetotransload Scenarios Unloading truck Loadingtruck Shippingcost Truckonly Transport loadingrail Emissioncost Fuel Surcharge unloadingrail Shippingcost Emissioncost Loading Loading 0 Shipping 48,141,282 Emission 1,566,196 Unloading 0 FuelSurcharge 8,216,112 Totaltruckcostexcludingemission 56,357,394 Totaltruckcostincludingemission 57,923,591 Multimodal transport TotalRailCost drayagetofinaldestination railhaul ifrailhasdirectaccestofinaldestination 0 27,710,326 901,509 0 13,209,240 52,914,942 391,467 13,209,240 ifraildoesn'thavedirectaccessanda drayage25miles 0 27,710,326 901,509 0 13,209,240 52,914,942 391,467 13,209,240 Shippingcost Emission cost Unloading Including Excluding Including Excluding emission emission emissionand emissionand and and including including excluding excluding drayageatthe drayageatthe drayageat drayageat end end theend theend 6,269,426 113,313,174 13,209,240 8,255,775 268,588 13,209,240 7,678,412 114,606,150 149,396,415 150,957,979 75 Appendix F: Example of calculating percent change of cost for using multimodal (excluding emission) ExcludingEmission Costperton(USD) UsingTransloadfacility (Withoutdirectaccessto rail) 67.86 Increaseincost pertonpercentif emissionis excluded ExcludingEmission TruckOnly UsingTransload facility(With directaccessto rail) TruckOnly 25.60 51.47 25.60 Ͳ165 Ͳ101 Appendix G: Example of calculating percent change of cost for multimodal (including emission) IncludingEmission Costperton(USD) Increaseincost pertonpercentif emissionis included UsingTransloadfacility (Withoutdirectaccessto rail) TruckOnly 68.57 26.31 Ͳ161 IncludingEmission UsingTransload facility(With TruckOnly directaccessto rail) 52.06 26.31 Ͳ98 76 Appendix H: Exhibit of percent cost savings for Wisconsin movements (negative value means increase in cost savings for multimodal) WisconsinMovements(withoutdirectrailaccess) Mackinac Schoolcraft Chippewa Luce Menominee Delta Iron Dickinson Gogebic Marquette Keewenaw Ontonagon Houghton Baraga Alger Ͳ140 Ͳ120 Ͳ100 Ͳ80 Ͳ60 Ͳ40 Percentcostsavings,% Amasa Ishpeming 77 Nestoria Ͳ20 0 Appendix I: Exhibit of percent cost savings for Sault Ste. Marie movements (with direct rail access at final destinations) (negative value means increase in cost savings for multimodal) SaultSte.MarieMovements(withdirectrailaccessatfinaldestination) Mackinac Schoolcraft Chippewa Luce Menominee Delta Iron Dickinson Gogebic Marquette Keweenaw Ontonagon Houghton Baraga Alger Ͳ500 Ͳ400 Ͳ300 Ͳ200 Ͳ100 Percentcostsavings,% Amasa Ishpeming 78 Nestoria 0 100 Appendix J: Exhibit of percent cost savings for Chicago movements (with 25 mile truck drayage at final destinations) (negative value means increase in cost savings for multimodal) 79 Appendix K: Exhibit of percent cost savings for Minneapolis movements (without direct rail access at final destinations) (negative value means increase in cost savings for multimodal) 80 Appendix L: Summary table of percent cost savings from each transload location (direct rail access at final destinations), if emission cost is excluded Wisconsin Nestoria Ishpeming Alger -20 -7 Baraga -39 -48 Houghton -40 -49 Ontonagon -76 -84 Keewenaw -34 -41 Marquette -26 -13 Gogebic -91 -102 Dickinson -74 -67 Iron -157 -97 -37 -21 Delta -105 Menominee -115 -15 -4 Luce -16 -5 Chippewa -13 Schoolcraft -26 -24 -14 Mackinac Counties Amasa -20 -28 -29 -6 -25 -28 -68 -43 -63 -26 -78 -15 -13 -22 -20 Chicago Nestoria Ishpeming 7 15 24 20 21 16 15 11 20 11 2 -6 -4 -42 0 -6 -9 0 Amasa 1 24 21 16 27 5 5 -4 6 -37 0 -6 -9 0 13 15 11 5 -3 -28 -5 -9 -11 -2 81 Minneapolis Nestoria Ishpeming Amasa 14 22 11 12 7 14 7 2 9 -7 -12 -4 7 3 8 17 25 13 -29 -35 -20 3 6 13 -5 -3 8 4 13 7 -27 -22 -13 5 6 5 4 7 7 5 8 5 5 6 5 Sault Ste. Marie Nestoria Ishpeming Amasa -84 -64 -112 0 -6 -9 0 -7 -9 -29 0 -20 -7 -8 -39 -177 -412 -100 -260 -16 -6 -15 -4 5 -33 -150 -367 -286 -312 -54 -3 -17 -3 -15 -34 -210 -459 -335 -385 Appendix M: Summary table of percent cost savings from each transload location (25 miles truck drayage at final destinations), if emission cost is included Counties Alger Baraga Houghton Ontonagon Keewenaw Marquette Gogebic Dickinson Iron Delta Menominee Luce Chippewa Schoolcraft Mackinac Wisconsin Nestoria Ishpeming Amasa -51 -81 -79 -123 -67 -63 -139 -120 -102 -72 -162 -39 -38 -56 -49 -38 -90 -88 -131 -74 -50 -150 -113 -152 -55 -153 -28 -28 -42 -39 -51 -70 -68 -107 -58 -65 -115 -90 -118 -60 -126 -39 -36 -51 -45 Chicago Nestoria Ishpeming Amasa -14 5 3 -5 -6 0 2 -9 -20 5 3 -4 -1 -9 -20 -31 -27 -70 -19 -25 -31 -19 7 -15 -20 -29 -17 -65 -19 -25 -31 -9 -7 -5 -12 -20 -26 -56 -24 -28 -33 -20 82 Minneapolis Nestoria Ishpeming Amasa -4 -9 -14 -31 -13 -2 -56 -19 -28 -16 -51 -8 -9 -13 -12 3 -14 -19 -35 -17 5 -62 -15 -26 -7 -46 -15 -9 -9 -7 -8 -7 -12 -27 -11 -7 -47 -9 -16 -13 -37 -11 -8 -13 -12 Sault Ste. Marie Nestoria Ishpeming Amasa -131 -30 -28 -111 -36 -34 -159 -39 -37 -67 -25 -51 -35 -34 -70 -234 -571 -145 -332 -53 -31 -47 -33 -22 -64 -207 -467 -349 -412 -92 -28 -49 -32 -41 -65 -267 -558 -424 -487
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